NSC92 成果報告 3G=922213E324019
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行政院國家科學委員會專題研究計畫 成果報告 第三代行動通訊網路資源管理之研究 計畫類別： 個別型計畫 計畫編號： NSC92-2213-E-324-019- 執行期間： 92 年 08 月 01 日至 93 年 07 月 31 日 執行單位： 朝陽科技大學資訊工程系(所) 計畫主持人： 張本杰 共同主持人： 黃仁竑 計畫參與人員： 林書宇,彭瑱瑞 報告類型： 精簡報告 處理方式： 本計畫可公開查詢 中 華 民 國 93 年 8 月 11 日
Transcript of NSC92 成果報告 3G=922213E324019
行政院國家科學委員會專題研究計畫 成果報告
第三代行動通訊網路資源管理之研究
計畫類別：個別型計畫
計畫編號：NSC92-2213-E-324-019-
執行期間：92年08月01日至93年07月31日
執行單位：朝陽科技大學資訊工程系(所)
計畫主持人：張本杰
共同主持人：黃仁竑
計畫參與人員：林書宇,彭瑱瑞
報告類型：精簡報告
處理方式：本計畫可公開查詢
中 華 民 國 93年8月11日
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行政院國家科學委員會補助專題研究計畫成果報告 ※※※※※※※※※※※※※※※※※※※※※※※※※
第三代行動通訊網路資源管理之研究
※※※※※※※※※※※※※※※※※※※※※※※※※
計畫類別：R個別型計畫 □整合型計畫 計畫編號：NSC-92-2213-E-324-019 執行期間：92年 08月 01日 至 93年 07月 31日
計畫主持人：張本杰 副教授 (朝陽科技大學 資訊工程系) 共同主持人：黃仁竑 教授 (國立中正大學 通訊工程系) 計畫參與人員： 林書宇 (朝陽科技大學 資訊工程系碩士班研究生)
彭瑱瑞 (國立中正大學 通訊工程系碩士班研究生)
成果報告類型(依經費核定清單規定繳交)：R精簡報告 □完整報告
本成果報告包括以下應繳交之附件： □赴國外出差或研習心得報告一份 □赴大陸地區出差或研習心得報告一份 □出席國際學術會議心得報告及發表之論文各一份 □國際合作研究計畫國外研究報告書一份 處理方式：除產學合作研究計畫、提升產業技術及人才培育研究計畫、
列管計畫及下列情形者外，得立即公開查詢
□涉及專利或其他智慧財產權，□一年□二年後可公開查詢
執行單位：朝陽科技大學資訊工程學系
中 華 民 國 93 年 8 月 1 日
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行政院國家科學委員會專題研究計畫成果報告
第三代行動通訊網路資源管理之研究
Resource Management for 3G Networks
計劃編號：NSC-92-2213-E-324-019
執行期限：92年 8月 1日至 93年 7月 31日
主持人：張本杰 朝陽科技大學資訊工程學系
Email: [email protected]
摘要 由於目前WCDMA系統下 OVSF碼樹正交
的特性，使得碼阻塞機率會隨著資料量或資料
傳輸率的增大而有所增加，這將造成頻道編碼
無效率的利用，因此如何有效地管理 OVSF碼樹下的頻道編碼資源，是目前已廣泛研究的重
要議題。然而在我們所提出的計畫中，著重於
如何有效率地配置頻道編碼，我們首先提出以
Markov decision process (MDP)為基礎的分析方法作有效率地配置頻道編碼，之後透過延伸
MDP-based的方法來當作允入控制機制，使系統穫得最大的利潤收益並且減少碼阻塞機率。
除此之外，目前 3GPP需求的資料傳輸率必須是 2的指數次方基本資料傳輸率，例如：1R、2R、4R…等，但是這並不符合實際網路狀況，如果需求的資料傳輸率不為 2的指數次方倍數將會導致系統頻寬的浪費，因此為了解決上述
問題，我們提出適性群組的頻道編碼配置方
式，提供單一頻道編碼給予任何需求的資料傳
輸率。在模擬的數值結果中，我們所提的MDP方法與其他方法比較的結果能有較小的部份盈
收損失及較低的碼阻塞機率，而且我們所提的
適性群組方法能明顯地減少浪費率，因而增加
系統資源的使用率。 關鍵詞: MDP， OVSF，WCDMA，碼配置，適性群組，時柵交錯
Abstract: Since the orthogonal characteristic of the
Orthogonal Variable Spreading Factor (OVSF) code tree in Wideband CDMA (WCDMA) systems, code blocking increases as traffic load or required rate increases. This causes
inefficient utilization of channelization codes. Hence, how to efficiently manage the resource of channelization codes of OVSF code tree in WCDMA is an important issue and has been studied extensively. Therefore, in this project we focus on how to assign channelization code efficiently. We first propose the Markov decision process (MDP) based analysis to assign channelization codes efficiently. Next, we extend the MDP-based approach as the call admission control mechanism to maximize system revenue while reducing blocking. Moreover, the required rate of traffic should be powers of two of the basic rate, i.e. 1R, 2R, 4R, etc., which is impractical and results in wasting the system bandwidth while the required rate is not powers of two of the basic rate. Therefore, we propose an adaptive grouping code assignment herein to provide a single channelization code for any possible rate of traffic, even though the required rate is not powers of two of the basic rate. Numerical results indicate that the proposed MDP approach yields the best fractional reward loss and code blocking reward loss as compare to that of other approaches. Furthermore, the proposed adaptive grouping approach reduces significantly the waste rate and thus increases the system utilization. Keywords: MDP, OVSF, WCDMA, Code assignment, Adaptive grouping, Slot interleaving
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I. Introduction For high speed communication and fast
Internet access with multimedia, the third generation (3G) mobile systems have been designed for this purpose in mobile cellular communication systems. Several 3G standards have been proposed such as Universal Mobile Telecommunications Service (UMTS) and cdma2000, which are led by the groups of Third Generation Partnership Project (3GPP) [1] and 3GPP2[2], respectively. In UMTS[3] which supports a wide rage of applications with different quality of service (QoS) requests, including higher bit rate on circuit-switched and packet-switched connections, streaming traffic, interactive traffic, and background traffic. For achieving that, UMTS covers two technical aspects: the technology of radio air interface and the UMTS networks. UMTS adopts WCDMA as air interface for supporting highly variable data rates. WCDMA [4] supports variable data rate ranging from 144Kbps for vehicular traffic services to 384 Kbps for pedestrian service and up to 2Mbps for fixed environment service. Consequently, the technique of OVSF is adopted, which allows the spreading factor to be changed and orthogonality between different spreading factors of different lengths. Moreover, OVSF is preferable to Orthogonal Constant Spreading Factor (OCSF) [5] at the aspects, including less complicated in hardware and supporting variable data rate with a single code. A general OVSF code tree is shown in Fig. 1.
Although OVSF has such several advantages, some restrictions apply to the use of channelization codes. That is because a call using a channelization code on the tree will block all of the channelization codes which are at the descendant branches of the allocated code and the channelization codes which are the ancestor codes of the allocated code. That results a new incoming call will be rejected by the OVSF code tree due to code blocking even though the residual system capacity of the tree is enough for the required bandwidth of the new incoming call.
Furthermore, the codes of the OVSF
code tree will become fragmented after operating the tree for a long period. In this situation, code blocking occurs frequently. However, more code blocking results in more blocking and less utilization. To improve code blocking, several reassignment mechanisms have been proposed in [6]. Nevertheless, the reassignment mechanisms bring too many overheads, including computation overhead, moving allocated channelization codes and long call setup time; moreover, Minn et al. [6] considered how to select a victim code to reassign, but not addressed where to allocate the victim code. Nevertheless, code reassignment brings several disadvantages. Thus, an efficient code assignment is necessary for reducing code blocking. Once the code blocking can be reduced, above mentioned drawbacks can be improved and blocking of the OVSF code tree also can be reduced. Consequently, the motivation of this project has two aspects. Firstly, we propose the MDP-based approach to allocate the channelization code efficiently of OVSF code tree in WCDMA systems while reducing the code blocking. Secondly, in most of previous researchs, the call admission control of the OVSF code tree systems were adapted the capacity-based mechanism as CAC. In such a call admission control mechanism, each traffic class brings the same reward. Since, different classes of traffic have different QoS requirements and thus bring different reward for the system. Accordingly, an efficient CAC mechanism is required to achieve high utilization while increasing reward, especially under the case of the residual capacity is critical for a new incoming call. Therefore, we extend the MDP approach as the CAC mechanism for the WCDMA network systems to improve the system reward. II. Related Works
Fantacci et al. [7] proposed a static code allocation scheme by reserving particular codes for high class traffic to improve the system utilization. Meanwhile, the reassignment scheme is adopted by moving the rightmost code to the terminated outgoing code at the same spreading factor level. A
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priority-based scheme is proposed [8] to assign lower required rate from left to right in the OVSF code tree and the higher required rate is from right to left. The same reassignment scheme is applied while there is a call departure. Both in [7] and [8], the non-global reassignment scheme can not completely meet the optimal utilization and may results in unnecessary reassignment. A region division assignment (RDA) was proposed in [9] that partitions the OVSF code tree into several regions. Each region is served for a specific data rate and allow a manner of borrow code from other regions. But the static scheme is not adequate to dynamic traffic as well as fragments still exist in regions. In [10], the crowded first assignment scheme is proposed to assign code for a new incoming call as crowded as possible. The algorithm finds the first crowded available code then assigns it to the new call. Then yields that the allocated code is not the most crowded code in the OVSF code tree, i.e. the crowded first scheme is local optimal, but not global optimal. An index-based assignment is proposed in [11], the concept of it is similar to [10]. But the complexity of index computation is higher than that in [10]. In [12] a weighted assignment scheme is proposed based on setting the number of upper and lower level codes that will be blocked if the code is assigned. Then define the weight of channelization code as the number of its ancestor codes that will be blocked and are not already blocked. Results show the blocking is almost the same as that in [10]. Above code assignment schemes are not adaptive to the dynamic traffic of connections. Above code assignment schemes are not
adaptive to the dynamic traffic of connections. Moreover, the theory of MDP is a pledge method in a lot of network-related issues. A number of network control schemes have been developed based on Markov decision process [13]. For instance, many MDP-based routing algorithms, which compute the link cost based on the MDP theory, have been proposed and shown to yield very good performance [14,15]. In [16], we have proposed the MDP approach for defining link cost functions in hierarchical networks. In this project, we will adopt the idea of MDP approach to define the cost of the OVSF code tree for its simplicity and efficiency, and then assign channelization code for a new incoming call based on the cost function. Furthermore, we extend the MDP approach to as a MDP-based CAC to improve the system total reward while reducing blocking. III. Two Efficient Code Assignment Approaches In this Section, we proposed two methods to improve wireless radio resources in 3G WCDMA air interfaces, including the MDP-based code assignment scheme and the time-sharing based adaptive grouping code assignment. They are described in detail as follows. A. MDP-based Code Assignment Scheme
Since there are two types of blocking in the OVSF code tree, including capacity blocking and code blocking. The former is instinctive from the capacity of the OVSF code tree; nevertheless, the latter is due to the orthogonal characteristic of variable data rate and inefficient code assignment. Furthermore, the OVSF code tree will become fragmented after operating for a long period and yields more code blocking. More inadequate code assignment results in more blocking, even though the residual capacity of the system is enough for the new incoming call. Therefore, the Markov Decision Process (MDP) based channelization code assignment scheme is proposed herein to reduce code blocking in the procedure of code assignment, and then apply
level, k
1
2
k
K-1
K
.
.
.
.
.
.
Spreadingfactor, sfk
1
2
2k-1
.
.
.
.
.
.
2K-2
2K-1
Rate, rk
(2K-1/2k-1)R
2R
1R
.
.
.
.
.
.
2K-2R
2K-1R
Figure 1: OVSF code tree
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MDP code assignment scheme as call admission control mechanism to improve utilization while increasing rewards. The MDP approach for managing the channelsization codes of the OVSF code tree is described in detail as follows.
Firstly, consider a single link E∈l in isolation. The state of the link is described by the link occupancy, and the link is assumed to handle K classes of traffic, each with different QoS requirements. First, the steady state distribution of the link is approximated by the steady state distribution of a birth-death process [17], as follows. Assume that the connections of class k traffic arrive at link l according to a Poisson process with rate l
kλ . Then, the connection duration is exponentially distributed with mean kµ/1 . Let kkk µλρ /ll = be the offered load for class k calls to this link. The birth-death process has state space {0,…, lC }, where lC denotes the capacity of the link l . When in state i, the process has a death rate of i and a birth rate of
( )222 /1/ σξσξλ −+= iil , where ∑ == K
klkkb1 ρξ
and ∑ == Kk
lkkb1
22 ρσ . Intitutively, ξ and 2σ are to capture the traffic load with the consideration of bandwidth requirements. The rewards are referred to [17] for further explanation. Let ,,...,0),( Cnn =π be the equilibrium probability of being in state n , satisfies
( ) ,,...,1),1(]/1/[)( 222 Cnnnnn =−−+= πσξσξπ (1)
and
∑=
=C
nn
0.1)(π (2)
When C approaches infinity, )(nπ has the Pascal distribution [17, 18].
By forming a Markov decision process on the birth-death process, the longterm average reward loss rate, g, and a set of relative values, )(iv , can be obtained, using the following set of simple expressions [19]:
,)1,(
),()1()(1
1
−=−−
−
∑ =
l
ll
l
lC
K
jlj
lj
ll
ll
CE
CErCvCv
l λ
λ
λ
λ (3)
lCiiE
giviv lli
ll <≤−
=−−−
1,)1,(
)1()(1 λλ
, (4)
where
,
!1
!1
),(
0
1
0
1
0
∑ ∏
∏
=
−
=
−
==
i
n
n
j
lj
i
j
lj
l
n
iiEλ
λλ
(5)
and ∑ =
−−−=K
j ll
ll
llj
lj CvCvCrg
1))1()((λ . (6)
The difference of relative values between two adjacent states (see Equation (4)) represents the difference of reward loss when the Markov process started from these different states. Therefore, in [19], the cost of adding a class k call when in the state i , denoted by )(ipk
l , is defined as:
∞≤+−+
=.
),(/))()(()(
otherwiselCapbiifivbiv
ip kkl
kl
lk
µ
(7)
Namely, the cost of adding a class k call will change the link state from i to kbi + , and thus the cost is the difference of relative values between states i and kbi + , and is normalized to connection holding time.
Equations (3)-(7) provide a simple way of obtaining the cost of carrying a class k connection on a link. However, traffic must first be classified, and then two parameters must be defined, l
jr and l
kλ . First, when focusing on a link in isolation, each connection will be characterized by its QoS requirement only, regardless of its O-D pair. Specifically, all connections that require the same amount of bandwidth are classified into the same traffic class. The problem is then how to define the link reward for a traffic class, i.e. the parameter ω
mr . Since connections with the same bandwidth may have different reward (at the connection level) and may be carried on different routing paths, the link reward is defined by on-line measurement. Specifically, for each class k connection carried by the network, the link reward is set as
Zbrr k
kk
l
l ×= ω ,
where ωkr is the original reward for this
connection, Z is the total bandwidth required to set up the connection on the routing path
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and l
kb is the bandwidth required at link l . The “modified” link reward for class k traffic is the average of the link reward obtained from each individual connection.
Second, the arrival rate l
kλ of class k traffic on link l is computed on-line based on the exponential smoothing model, as follows:
( )l
l
ll
k
koldknewk B−
⋅+⋅−=1
~1 ,,
λαλαλ
, where l
kλ~ denotes the average number of
class k calls carried on link l per unit of time, which is updated at regular time intervals, l
newk ,λ is the new predicted arrival rate, l
oldk ,λ is the previous value, l
kB is the proportion of time the class k traffic is blocked, and α is a constant from (0, 1).
In summary, based on the MDP approach, we define the cost of carrying a class k call on link l when the link occupancy is i as
)(ip lk . Now, we model an OVSF code tree in
WCDMA 3G networks, ),( SFT β= , which belongs to a Node B system, β , and with maximum spreading factor, SF . The OVSF code tree has height, H , and level, Hk ,...,1= , where root of tree is at level 1=k and leaves are at level Hk = . The total number of level or class is denoted as K , which is the same as H . The rate of each leaf code is R1 and that of up one level is doubled. Hence, required rate of level k with spreading factor ksf is RsfSF k )/( and rate of root code is
RH 12 − or RSF ⋅ . The total capacity of the OVSF code tree is denoted as TC , where
RSFCT ⋅= . Consideration of multirate traffic, different classes require different rate. A class k call requires a channelization code at level k and the required rate is thus RsfSF k )/( . The state of level k channelization code, ki , can be viewed as the tree is occupied by the codes that are all class k calls. Nevertheless, an incoming class k call requires a code at level k will block all codes on the path from the specified code to root and all branch codes of the specified code. In other words,
the success allocating code impacts on not only the state of level k , but also the state of other levels of the tree.
The MDP approach models each level state of an OVSF code tree T with capacity
TC as a Markov process with a birth rate of ikλ and death rate of i
kµ , where k is level and i is occupancy of level k or denoted more specifically as ki . The state-transition-rate diagram for the Markov Decision Process of the MDP approach is shown in Fig. 2. Let
,,...,2,1,0),( Tkk
kk CsfSF
sfSFii ⋅⋅=π where
kkT sf
SFsfC ⋅= ,
be the equilibrium probability of being in state ki of level k , satisfies
,,...,1),)(()( Tk
kkk
ikk
ik C
sfSFi
sfSFii ⋅=−= πλπµ (8)
and
∑=
=T
k
C
iki
0
.1)(π (9)
Therefore, we model the cost of carrying a call of class k on a OVSF code tree T with level k occupancy ki as the sum of each level cost from level k to the root level. Based on the MDP approach, the level cost is defined as
∞
≤+−+=
.
),(/))()(()(
otherwise
TCapRsfSFiifivR
sfSFiv
ip kkkk
T
kk
T
kTk
µ
(10)
Since, an incoming class k call will block all codes on the path from the chosen code to root and all branch codes of the chosen code. Moreover, the cost of the chosen code’s paranet can be represented as the state of the brach; hence, the cost of carrying class k call can be defined as the summation of all level cost from the chosen code’s parent level to the root level. Consequently, the cost of carrying a class k call on the OVSF code tree T with level k occupancy ki , )( k
Tk iΓ , is defined as
,)()(1
∑=
=Γkj
k
T
jkTk ii p (11)
where j is the tree level and root is at level 1. If there are several available channelization codes at level k , the channelization code with
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minimum cost will be chosen as the allocated code for the incoming call.
B. Time-sharing Based Adaptive Grouping Code Assignment
In current 3GPP specifications, it adopts a single channelization code assignment for each incoming call, which causes the waste rate and inefficient utilization as the required data rate is not powers of two of the basic rate. This movivates us to propose the grouping approach with the dynamic OVSF code assignment scheme to overcome the waste rate problem while providing a single channelization code for an incoming call. Especially, the data rate of the call can be all the possible data rate even though the data rate is not powers of two of the basic rate. The main concept of the grouping mechanism is to share a subtree for a group of calls. In 3GPP, the spreading factor of each member can be changed dynamically per frame of 10ms. The system can provide each call different code rate at different frame time. As a result, the average data rate of the call during the cycle time will satisfy the required rate and it thus eliminates the waste rate. Nevertheless, the proposed grouping approach may suffer from packet delay. The delay and delay jitter become worse as the length of a group cycle increases. Therefore, we propose the interleaving scheme to reduce delay and jitter. In this section, we first describe the approach of grouping with the dynamic code assignment scheme. Then we explain the proposed cycle interleaving scheme to reduce the delay and delay jitter in a cycle.
1. Grouping Mechanism Group is a set of calls or members and a
group has at least one member. The data rate of a group is summed by the data rate of each call within the group. Hence, the system allocates a subtree to the group, in which the code rate of the subtree matchs the group rate. Based on the time-sharing concept, each group is operated at a specified cycle length as well as each call within the group is
assigned various code rate at part of time slots in a cycle. We assume that the data rate of a group g is
gR , where RRR ng
n 22 1 ≤<− . A subtree with the data rate of Rn2 can be assigned to the group g and all members of the group share all channelization codes in the subtree. Consequently, the group does not yield any waste rate when the group rate is the same as the code rate, i.e. RR n
g 2= . Since there are four 64R-subtree in an
OVSF code tree, we consider the data rate of group ranging from 1R to 64R except those groups that can be reduced to subgroups. In a group, the data rate of all members are sorted in descending order. For reducing the time of forming groups, the group look-up table is created by pre-computing all possible groups. The cycle lengths of all groups of a specific group rate are sorted in ascending order. The cycle length is the number of time slots (or frames) within it. To reduce the table size is necessary for the system to limit the number of members in a group and load the table to the memory. It thus speeds up the searching time while accessing the look-up table and reduces the required memory to store the look-up table.
2. Dynamic OVSF Code Assignment After forming several calls in an OVSF
code tree system into a group, the system generates cycle for the group. According to the cycle, each member of the group is assigned dynamically to the specified code slot by slot. As a result, the average data rate of a call member in the cycle satisfies its required rate. The simplest group is that the group has only one member within it. In the case of two members or above within a group, the system generate cycles based on the dynamic programming algorithm, in which a cycle is determined by combining the cycles to the sub-problems that are dependent. The process of determining the group cycle is described in the following three cases.
1) The case of only one member within a group
Since a group g has only one member
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xM with the required rate of XR , where RXRR nn 22 1 ≤<− , the cycle length should be
one time slot. The waste rate is XRRn −2 and it becomes 0 if RXR n2= .
2) The case of two members within a group A group g consists of two members, xM
and yM , with the required rate of XR and YR , respectively. The group rate is thus YRXRRg += , where RRR n
gn 22 1 ≤<− .
Accordingly, we partition the problem into two aspects, i.e. the aspect of RR n
g 2= and the aspect of RRR n
gn 22 1 <<− . First, in the aspect
of RR ng 2= , the cycle time is determined as
follows. Let nYX 2=+ , where YX < and
nn Y 22 1 <<− . We have YC n −= −11 2 and
YC n −= 22. Assume that the least common
multiple (LCM) of 1C and 2C is C , i.e. ),( 21 CCLCMC = . This induces the member yM
needs a slots of Rn 12 − and b slots of Rn2 , where 1/ CCa = and
2/ CCb = . Finally, the cycle length of the group, g , is thus baL += . In other words, the cycle of member
yM consists of a slots of Rn 12 − code and b slots of Rn2 code of the 1+− nK level and the nK − level, respectively. When the member yM uses Rn 12 − code (where
RYRn ⋅<−12 ), it is short of RCRYn1
12 =−− rate bandwidth to transmit. Meanwhile, when the member yM uses Rn2 code (where
RYRn ⋅>2 ), it exceeds RCRYn22 =− rate
bandwidth. Finally, the cycle length is ba + and the data rate of member yM consists of
Ra n 12 − codes and Rb n2 codes at different time slots, which results in that the average rate of member yM within a cycle time is the same as the required rate of member yM , i.e. YRLRbRa nn ⋅=⋅+⋅ − 22 1 . The proof is shown in appendix.
On the other hand, the member xM has the same cycle length L , but it only consists of a slots of Rn 12 − codes and b slots of
R0 within the cycle. The average rate of member xM is the same as the required rate of it, i.e. XRLRbRa n ⋅=⋅+⋅ − 02 1 . Consequently, the average data rate per frame of member
xM and yM are XR and YR , respectively. The proof is also shown in appendix. Furthermore, the cycle length of a n-level OVSF code tree, L , is bounded at 11 22 −≤≤ nL . Hence, the cycle length is small when the LCM C is smaller.
For instance, a group consists of two members of R3 and R5 rate, where the group rate
gR is R8 , where RRR g 84 ≤< . The cycle is determined as follows.
1/3/
3)3,1(3,1
8545,3
2
1
21
====
====
≤<==
CCbCCa
LCMCCC
YXQ
The result of the allocated rate of a and b slots for memebers xM and yM within a cycle is shown in Fig 2. The average data rate within a cycle of memebers xM and yM are
RR 34/12 = and RR 54/20 = , respectively. Second, in the case of RRR n
gn 22 1 <<− ,
since gR is less than Rn2 , the cycle length should be smaller than the case of RR n
g 2= . In other words, the slots of a and b must less than or equal to that of RR n
g 2= . The determination of a and b is described as follows.
Let YXYX nn <<+<− ,22 1 , where YX < and nn Y 22 1 <<− . We have YC n −= −1
1 2 and
YC n −= 22 . Assume that the member xM needs a slots of Rn 12 − code, where a is selected starting from one to 1C , to satisfy
XRaRa n ⋅≥⋅ −12 . This induces the member yM needs b slots of Rn2 , where 21 / CaCb ⋅= . Finally, the cycle length of the group, g , is thus baL += , where 122 −<≤ nL . In other words, the cycle of member yM consists of b slots of Rn2 code of the nK − level. Consequently, the average data rate of members xM and
yM should not less than XR and YR ,
9
respectively. For instance, a group consists of two
members of R2 and R5 rate, where the group rate gR is R7 and RRR g 84 << . Then the cycle is determined as follows.
13/1113,1
5,2
21
=⋅=∴=
====
baselected
CCYX
The slots of a and b of the memebers xM and yM is shown in Fig. 3. The average data rate within a cycle of memebers xM and
yM are thus RR 22/4 = and RRR 62/)84( =+ , respectively. Notely, the
average rate for the member yM is 6R, which is enough for the required rate of 5R.
3) The case of three or more members within a group
In the case of three or more members within a group, a reduction process will be performed before determining the cycle length of a group. The reduction process works based on the algorithm of dynamic programming, in which a group cycle is determined by combining the cycles to the sub-problems. The proposed approach for determining the group cycle includes two processes, including the reduction process and the decomposition process.
First, the reduction process is adopted to reduce the members to twos, and then
generates cycles according to the solution of the case of two members within a group. The main concept of the reduction process is to find the minimum cycle length from the solutions of two members. Second, the process of decomposition is used to decompose one cycle generated by reduction to two cycles and the rest may be deduced by analogy. Finally, the cycle of the group with three or more members can be determined.
As shown in Fig. 4, a group has four members aM , bM , cM , and dM . The required data rate of them are AR , BR , CR , and DR , respectively. First reducing four members to two members xM and yM , the member xM consists of aM and bM , and the member yM consists of cM and dM . Then the slots of xM and yM , a and b , can be determined based on the method of two members. Since each member of xM or yM consists of another two members, the cycles of
xM and yM can be divided properly to obtain the final cycle for each member. In other words, the codes of each member cycle,
Rn 12 − and Rn2 , may be divided into two Rn 22 − codes and two Rn 12 − codes,
respectively. Therefore, by decomposing the cycles of xM and yM , the cycle slots of aM ,
bM , cM , and dM can be determined eventually. The decomposition process is achieved as follows. In xCycle , since the data rate of b slots is R0 , the a slots should be decomposed. As viewed by the aM member, we assume that the a slots consist of α slots of Rn 22 − rate and β slots of R0 rate, in which it satisfies
=+⋅+
⋅=
+ −
−
.2)(
21
2
aBA
An
n
βαβα
α
After obtaining α and β , the decomposed data rate of member aM and bM can be determined as shown in Fig. 4.
In yCycle , the data rate of the b slots is
Rn2 , which can not be decomposed into two Rn 12 − codes for the reason of the solution of
two members. Therefore, the b slots may be
Fig. 2. The cycle of a two-member group with rate of RR n
g 2=
Fig. 3. The cycle of a two-member group with rate of
RRR ng
n 22 1 <<−
10
decomposed into Rn2 and R0 codes. As viewed by the dM member, we assume that the b slots consist of α slots of R0 rate and β slots of Rn2 rate, in which it satisfies
).(_
2)(2
ββ
βαβα
β
offroundb
DCD
n
n
=
=+⋅+
⋅=
+
Moreover, the average data rate of the members cM and dM may not meet their required data rate, CR and DR . A tune mechaism will be performed to compensate for the lack rate from the surplus rate of the a slots. Consequently, the final cycle slots of each member are determined after performing the decomposition process.
For instance, as shown in Fig. 5 is an example of slots determination of a four-member group with the required rate are 2R, 4R, 3R, and 7R, respectively. The 2R and 4R are grouped as the member xM , and 3R and 7R are grouped as the member yM . The cycle of a two-member group can be determined by the method of the case of two members,
.1/
3/6)6,2(
6,216108
10,6
2
1
21
==
====
==<<
==
CCband
CCaLCMC
CC
YXQ
Since the member xM consists of the members aM and bM , the cycles of xM can be decomposed by
.1,23
8)(4
422
==∴
=+⋅+
⋅=
+
βα
βαβα
α
Meanwhile, the member of yM consists of the members cM and dM , the cycles of yM can be decomposed by
.01)7.0(_
116)(
1673
7
=∴==
=+⋅+
⋅=
+
αβ
βαβα
β
offroundQ
Finally, the cycle slots of all members,
aM , bM , cM , and dM , are determined, which are shown in the final result table in Fig. 5.
3. Cycle Interleaving The main idea of the proposed
time-sharing approach is to share a single channelization code among several calls of a group; meanwhile, it provides the long-term average rate for each member to satisfy its required rate. A cycle of a member call consists of several time slots, in which the allocated data rate for the member may be higher or lower than the required rate. Therefore, the proposed time-sharing mechanism needs some buffers to queue the data that is not transmitted at the slots that the code rate is lower than the required rate. The delayed data results in packet delay and delay jitter, which are the main disadvantages of the
Mx(Ma and Mb)
(i.e. X=A+B)
2n-
12n-
1
2n-
1 0
2n2n-
1
2n-
12n-
1
2n-
1
2n-
1
2n-
1
2n-
1
2n 2n
0 0
My(Mc and Md)
(i.e. Y=C+D)
Ma
2n-
22n-
1
0 0
02n-
2
2n-
22n-
2
2n-
2
2n-
2
2n-
2
2n-
2
0 0
0 0
Mb
Mc
2n-
22n-
1
0
02n-
2
2n-
22n-
2
2n-
2
2n-
2
2n-
2
2n-
2 0 0
Md
2n
2n 2n
β
βα
α
Fig.4. The cycle slots generation after decomposing
Fig. 5. The example of the cycle slots generation of a 4-member group
11
proposed approach. To overcome the disadvantages, we propose herein the uniform and random interleaving schemes to rearrange the time slots within a group. Since the system will queue the data at the time slots with 0R code, the 0R slots should not be placed continous in the cycle. The primary idea of the proposed interleaving approach is to separate randomly or uniformly these 0R time slots. As a result, it reduces data delay, delay jitter, and buffer size of the system.
We consider two cases to rearrange these 0R slots, including the cases of ba ≥ and
ba < . First, in the case of ba ≥ , the time slots of the 0R rate at the subcycle of “a” period are rearranged uniformly. Then each time slots in the subcycle of “b” period are inserted to the rearranged subcycle of “a” period. Second, in the case of ba < , the time slots of 0R rate at the subcycle of “a” period are rearranged uniformly. Then the time slots of the subcycle of “a” period are inserted to the subcycle of “b” period. Consequently, these time slots of 0R rate are scattered uniformly on the cycle and thus reduce the buffer size, jitter, and packet delay. IV. Numerical Results
This section investigates the performance of these two proposed approaches. Fig. 1 shows the OVSF code tree for the evaluation, which has seven levels with maximum spreading factor, 64=SF . Assume that the data rate of all leave codes are 1R, then total capacity of the OVSF code tree, TC , is thus
R64 . In the MDP-based scheme, we first
evaluate the MDP-based assignment scheme with the Left Most and Crowded First schemes by comparing CBRL of that. Figs. 3-5 show the CBRL results under various traffic patterns, in which the MDP-based scheme yields the best CBRL and Left Most is the worst one. Especially, in Fig. 6, the CBRL of the Left Most scheme is better than that of the Crowded First scheme as traffic load is below 14. The reason is that the Pattern-A-3 has more amount of low request rate than the other traffic patterns. The Left
Most scheme is adaptive for more amount of low request rate, but the Crowded First scheme does not perform well under this case. Nevertheless, the proposed MDP-based scheme is not impacted on traffic loads and results in the best CBRL among them.
Second, FRL of MDP-based scheme with and without MDP CAC are evaluated. In conventional networks, most of all use the totally capacity as the admission control basis. Nevertheless, different classes of traffic or request bring different rewards. Therefore, in this project, we extend the MDP approach as the CAC of the OVSF code tree to achieve the main advantage, i.e. enhances utilization while brings the most reward. Figs. 7-9 show FRL of MDP-based assignment scheme with MDP CAC mechanism and with conventional capacity CAC under various traffic loads, including Pattern-B-1, Pattern-B-2, and Pattern-B-3, respectively. In Fig. 7, we consider four classes of traffic, in which the MDP CAC yields better FRL than that of capacity basis. FRL of five classes of traffic under different traffic patterns are evaluated as shown in Figs. 8 and 9. The amount of high request rate of traffic Pattern-B-2 is more than that of traffic Pattern-B-3. In both cases, the MDP CAC yields obviously better FRL than that of capacity basis; especially, the difference between both cases becomes significantly while the amount of high traffic rate increasing.
In the time-sharing code grouping code assignment, the average buffer size per connection and the average packet delay of the grouping table under various interleaving schemes are evaluated as shown in Figs. 10-11. Both in Figs. 10-11, the uniform interleaving scheme yields the least required buffer size and the least packet delay. Meanwhile, the non-interleaving scheme has the worst results in Figs. 10-11.
Then the performance of the proposed time-sharing single code assignment approach is evaluated by several performance metrics and compared it with the left-most single code assignment (LM) as shown in Figs. 12-16. For the reason of the proposed uniform interleaving scheme outperforms others. We
12
choose it as the interleaving scheme to evaluate performance metrics of two adaptive regrouping mechanisms, in which the system forms group based on required the least available bandwidth for the incoming call. In addition, as the number of members of a group increasing, the waste rate can be reduced significantly but the cycle length may be lengthened. The number of members of a group is thus a trade-off parameter. In this project, we evaluate different number of members of ranging from two to five, and without limitation, which are denoted as m_2,…, m_5, and non_limit, respectively.
In Fig. 12, we show a comparison of the system utilization of two grouping mechanisms with different number of members and the LM code assignment under arrival rate from 4 to 20. The higher the system utilization there is, the less the waste rate of assigning codes. From Fig. 12, we can see that LM has the lowest system utilization ranging from 0.835 to 0.94. In both FR and GR, the system utilization increases as the number of members increases. The proposed approach with FR grouping mechanism yields higher utilization than that with GR. Especially, FR_m_3, FR_m_4, FR_m_5, and FR_no_limit almost yield 100% the system utilization, on the other words, they do not waste the rate when the required rate is not powers of two of the basic rate.
In Fig. 13, FRL are compared among various approaches. Less FRL means better performance. LM has the highest FRL. FR yields the lowest FRL, which is lower than that of GR. Nevertheless, the number of members of a group does not affect FRL.
Since, FR generates more groups than that of GR, it needs more system buffer to queue those data packets that are not transmitted at the slots with lower code rate. As a result, the required system buffer, the connection delay, and the delay jitter of FR are higher than that of GR, which are shown in Figs. 14-16, respectively. Specifically, even though FR regroups all calls in the system as event arriving or departing to reduce significantly the waste rate, it results in needing more system buffer, and yielding
longer connection delay as well as longer delay jitter. Furthermore, the required system buffer, the connection delay, and the delay jitter of FR and GR decrease while the number of members of a group decrease.
V. Project Evaluation In this project, we propose two code
assignment approaches to improve the utilization of wireless radio resource. First, the MDP-based analysis approach is proposed to accomplish assigning channelization codes efficient and thus reduce code blocking significantly. Second, we propose the time-sharing based code grouping assignment to reduce the waste rate significantly, when the required rate is not powers of two of the basic rate. According to the valuable results of the project, we have submitted and published several results of the project to international conferences and journals. For instance, the former approach has published in IEEE ISCC 2004 (See Appendix) and the approach with considering MDP-based Call Admission Control mechanism has been submitted for international Journal. On the other hand, the approach of time-sharing code grouping assignment has been submitted for publications. VI. References [1] 3GPP, http://www.3gpp.org. [2] TR 45.5 “The cdma2000 ITU-RTT Candidate Submission,” TR
45-ISD/98.06.02.03, May 1998. [3] Universal Mobile Telecommunications System (UMTS),
Requirements for the UMTS Terrestrial Radio Access System (UTRA) Concept Evaluation, ETSI Technical Report, UMTS 30.06 version 3.0.0, Dec. 1997.
[4] E. Dahlman, B. Gudmundson, M. Nilsson, and A. Skold, “UMTS/IMT-2000 based on wideband CDMA,” IEEE Communication Magazine, vol. 36, issue 9, pp.70-80, September 1998.
[5] V.K. Garg, IS-95 CDMA and cdma2000. Prentice Hall, 2000. [6] T. Minn and K.-Y. Siu, “Dynamic Assignment of Orthogonal
Variable-Spreading-Factor Codes in W-CDMA,” IEEE Journal on Selected Areas in Communications, vol. 18, no. 8, pp. 1429-1440, August 2000.
[7] R. Fantacci and S. Nannicini, “Multiple Access Protocol for Integration of Variable Bit Rate Multimedia Traffic in UMTS/IMT-2000 Based on Wideband CDMA,” IEEE Journal on Selected Areas in Communications, vol. 18, no. 8, pp. 1441-1454, August 2000.
[8] W.-T. Chen, Y.-P. Wu, and H.-C. Hsiao, “A Novel Code Assignment Scheme for W-CDMA Systems,” IEEE Vehicular Technology Society Conference, vol. 2, pp. 1182-1186, 2001.
[9] R. Assarut, K. Kawanishi, U. Yamamoto, Y. Onozato, and M.
13
Masahiko, “Region Division Assignment of Orthogonal Variable Spreading-Factor Codes in W-CDMA,” IEEE Vehicular Technology Conference, pp. 1884-1888, 2001.
[10] Y.-C. Tseng, C.-M. Chao, and S.-L Wu, “Code Placement and Replacement Strategies for Widebane CDMA OVSF Code Tree Management,” IEEE Globecom’01, vol1. pp. 562-566, 2001.
[11] Y. Yang and T.-S.P. Yum, “Nonrearrangeable Compact Assignment of Orthogonal Variable-Spreading Factor Codes for Multi-Rate Traffic,” IEEE Vehicular Technology Conference, pp. 938-942, 2001.
[12] A.-N. Rouskas, and D.-N. Skoutas, “OVSF Codes Assignment and Reassignment at the Forward Link of W-CDMA 3G Systems,” PIMRC 2002, pp. 2404-2408, 2002.
[13] R. A. Howard, Dynamic programming and Markov processes. John Wiley & Sons, Inc., 1960.
[14] Ren-Hung Hwang, James F. Kurose, and Don Towsley, “MDP Routing for Multirate Loss Network,” Computer Networks, vol. 34, pp. 241-261, 1999.
[15] Ren-Hung Hwang, James F. Kurose, and Don Towsley, “MDP routing in ATM Networks Using Virtual Path Concept,” INFOCOM’94, 1994.
[16] Ben-Jye Chang and Ren-Hung Hwang, “Analysis of Adaptive Cost Functions for Dynamic Update Policies for QoS Routing in Hierarchical Networks,” Information Sciences, Volume 151, pp. 1-26, May 2003.
[17] S.-P. Chung and K.W. Ross, “Reduced Load Approximations for Multirate Loss Networks,” IEEE Transactions on Communications, Vol 41, No. 8, August 1993.
[18] R.I. Wilkinson, “Theories of toll traffic engineering in the USA,” Bell Syst. Tech. J., vol 40, pp 421-514, 1956.
[19] Ren-Hung Hwang, James F. Kurose, and Don Towsley, “State Dependent Routing for Multirate Loss Network,” Globecom’92, pp. 565-570, 1992.
Fig. 6. CBRL under traffic Pattern-A-1
Fig. 7. CBRL under traffic Pattern-A-2
Fig. 8. CBRL under traffic Pattern-A-3
Fig. 9. FRL under traffic Pattern-B-1
14
0
10000
20000
30000
40000
50000
60000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64
Group data rate
Ave
rage
con
nect
ion
buffe
r (by
tes)
Non-interleaving
Random interleaving
Uniform interleaving
Fig. 10. Average connection buffer of different interleaving schemes
under various group rate
0
2
4
6
8
10
12
14
16
18
20
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64
Group data rate
Ave
rage
pac
ket d
elay
per
con
nect
ion
(mse
c)
Non-interleaving
Random interleaving
Uniform interleaving
Fig. 11. Average packet delay of different interleaving schemes under
various group rate
0.80
0.82
0.84
0.86
0.88
0.90
0.92
0.94
0.96
0.98
1.00
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Arrival rate
Sys
tem
util
izat
ion
LM
FR_m2
FR_m3
FR_m4
FR_m5
FR_non limit
GR_m2
GR_m3
GR_m4
GR_m5
GR_non limit
Fig. 12. System utilization of different code assignment schemes under
various arrival rate
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Arrival rate
Frac
tiona
l rew
ard
loss
LM
FR_m2
FR_m3
FR_m4
FR_m5
FR_non limit
GR_m2
GR_m3
GR_m4
GR_m5
GR_non limit
Fig. 13. Fractional reward loss of different code assignment schemes
under various arrival rate
30000
50000
70000
90000
110000
130000
150000
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Arrival rate
Aver
age
syst
em b
uffe
r (by
tes)
FR_m2FR_m3FR_m4FR_m5FR_non limitGR_m2GR_m3GR_m4GR_m5GR_non limit
Fig. 14. Average system buffer of different code assignment schemes
under various arrival rate
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Arrival rate
Ave
rage
pac
ket j
itter
per
con
nect
ion
(mse
c)
FR_m2
FR_m3
FR_m4
FR_m5
FR_non limit
GR_m2
GR_m3
GR_m4
GR_m5
GR_non limit
Fig. 15. Average packet jitter per connection of different code assignment schemes under various arrival rate
15
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Arrival rate
Ave
rage
pac
ket d
elay
per
con
nect
ion
(mse
c)
FR_m2 FR_m3 FR_m4 FR_m5 FR_non limitGR_m2 GR_m3 GR_m4 GR_m5 GR_non limit
Fig. 16. Average packet delay per connection of different code
assignment schemes under various arrival rate
